Developing an AI Factory Digital Twin with an Agent-Assisted Workflow

Building an AI factory digital twin requires developers to bring site plans, equipment assets, and engineering requirements into a consistent model that teams can inspect, revise, and prepare for simulation. Connecting these inputs into an editable workflow helps teams explore design alternatives and incorporate engineering feedback as the design evolves. The NVIDIA Omniverse DSX Blueprint, part of NVIDIA DSX Sim, provides reference digital twin assets, sample scripts, and workflows for developing AI factory digital twins. Built on OpenUSD and NVIDIA Omniverse libraries, the blueprint gives developers a foundation for assembling facility and equipment models with SimReady assets, and connecting them with engineering simulation tools. Frontier AI models can help developers work with this foundation through agents and connected applications and tools, as explained in Into the Omniverse: How Developers Turn Ideas Into Simulations with Frontier AI Agents. Developers guide agents to bring together design inputs, assemble and revise OpenUSD scenes, and inspect the results. The blueprint supplies reusable assets and workflows, and the agents help developers apply and adapt them to specific design requirements (Figure 1). This post demonstrates an agent-assisted workflow for turning reference assets, site data, and design requirements into an editable OpenUSD campus model. Developers can use this approach to explore layouts, revise equipment placement, and prepare designs for engineering review and downstream simulation. Developers connect tools and guide changes, while engineers review results to inform the next iteration. Figure 1. Reference workflow from requirements through an OpenUSD digital twin to an engineering simulation, with results informing the next design revision Providing agents with a design foundation for AI factory modeling Consider a practical question: How should an AI factory hall be configured when the available power is fixed? Changing the compute configuration can impact equipment quantity, placement, electrical distribution, and cooling. Evaluating the alternatives requires those disciplines to work from consistent requirements and design data. An agent needs context before it can make a useful design proposal. Given only photos or renderings, it can produce a model that looks plausible but may have incorrect dimensions and proportions. Reference assets and project documents help ground the proposal, but the resulting model still …